Potential benefits of devices, systems and methods for measurement of chemical components or “analytes” in breath are known. Measurement of breath historically, however, has been done using relatively large and expensive laboratory equipment in a controlled lab or clinical setting. Recently there has been a drive to increase the feasibility and practicality of measuring breath by lowering equipment size, requirements, logistics and costs. The assignee of the present invention, for example, has developed a line of small, portable, relatively inexpensive and yet highly-accurate breath acetone analysis devices that reliably measure breath acetone in the low concentrations typically encountered in health and medical applications.
The state of the art in breath analysis has generally been directed towards the development of specific and selective sensors to quantify analytes—usually a single analyte—in breath. Individuals skilled in the art typically come from varying backgrounds of bioinstrumentation, chemistry, and physiology. Innovation in this industry has been further restricted by federal regulations. Such requirements often direct developers towards accepted thinking paradigms and often restrict or disincentivize cross-disciplinary efforts, especially those with unregulated industries, such as software, gaming, and artificial intelligence. Accordingly, there are substantial aspects of linkage between breath analysis devices and storage or centralized databases that are previously underdeveloped. As an example, there is no centralized information regarding the output of breath analyzers that facilitates bi-directional communication with the user for enhanced reporting.
Innovation in breath analysis is further challenged by the paucity of clinical relevance data, i.e., data establishing the relevance of the particular technology and the willingness of the applicable clinical community to adopt it. Although it is often cited that there are on the order of 300 analytes present in human breath, few (e.g., perhaps around ten) are generally clinically recognized. Procuring clinical relevance data is difficult, from the perspective of time, effort and money. Gas analysis tools, such as selected ion flow tube mass spectroscopy (SIFT-MS) and gas chromatography and mass spectroscopy (GC-MS), require large capital expenditures and trained technicians, which substantially reduce the volume of data that is available. The state of the art, therefore, is directed towards building devices that measure breath analytes (again, typically a single analyte) within a hypothesized analyte concentration range that one expects to encounter when the analyte measurement is made in a clinical context.
Ketone measurement has been studied but, as with breath analysis, has been primarily restricted to laboratory analysis. In recent years, point-of-care ketone analyzers, e.g., urine dipstick tests, have been commercially introduced, but those tests have been performed on a one-off basis, typically for preventing diabetic complications, e.g., such as diabetic ketoacidosis, perhaps 8-12 times per year. Systems and methods are needed that enable management of ketone data sets that are collected on a much more frequent basis, e.g., daily or multi-daily, such as 365 to 730 times a year. The significant increase in the number of measurements necessitates superior analysis and interpretation techniques.
Without the existence of commercially-viable and widely-deployed breath analysis devices, solutions directed to optimizing user interface, user feedback, clinical relevance and others have yet to be provided.
Breath analysis also has been limited in that multiple variables can affect the measurement results. Given the relative newness of the field and the relative paucity of commercial devices available for breath analysis, little attention has been paid to the variance of measurements and the variables that affect or give rise to those variances.